{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {},
   "outputs": [],
   "source": [
    "import os\n",
    "import tensorflow as tf\n",
    "os.environ[\"CUDA_VISIBLE_DEVICES\"] = '3' #use GPU with ID=0\n",
    "config = tf.ConfigProto()\n",
    "config.gpu_options.per_process_gpu_memory_fraction = 1 \n",
    "config.gpu_options.allow_growth = True \n",
    "sess = tf.Session(config=config)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "Using TensorFlow backend.\n"
     ]
    }
   ],
   "source": [
    "import numpy as np\n",
    "import pandas as pd\n",
    "import keras\n",
    "# import tensorflow as tf\n",
    "from datetime import datetime\n",
    "from sklearn import datasets, linear_model\n",
    "from matplotlib import pyplot as plt\n",
    "from matplotlib import cm\n",
    "from keras.layers.core import Dense, Activation, Dropout\n",
    "from keras.layers.recurrent import LSTM\n",
    "from keras.layers import BatchNormalization, Input, Embedding, Concatenate, Conv1D, MaxPooling1D, Flatten, merge\n",
    "from keras.layers import merge, Concatenate, Permute, RepeatVector, Reshape,Multiply\n",
    "from keras.models import Sequential, Model\n",
    "import keras.backend as K\n",
    "import statsmodels.formula.api as smf\n",
    "from keras.callbacks import ModelCheckpoint\n",
    "from keras.preprocessing.text import Tokenizer\n",
    "from keras.preprocessing.sequence import pad_sequences\n",
    "\n",
    "# prevent tensorflow from allocating the entire GPU memory at once\n",
    "config = tf.ConfigProto()\n",
    "config.gpu_options.allow_growth=True\n",
    "sess = tf.Session(config=config)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {},
   "outputs": [],
   "source": [
    "NUM_LAGS=10#滞后时间"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# 导入电站数据"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "station_1\n"
     ]
    }
   ],
   "source": [
    "station=pd.read_csv('station1.csv')\n",
    "x=station.loc[station.index<365]\n",
    "y=station.loc[station.index<186]\n",
    "station['time1']=pd.to_datetime(station['time'], format='%Y-%m-%d %H:%M').dt.strftime(\"%Y-%m-%d\")\n",
    "# station.index=pd.to_datetime(station['time'], format='%Y-%m-%d %H:%M')\n",
    "station['week']=pd.array(pd.to_datetime(station['time'], format='%Y-%m-%d %H:%M')).weekday\n",
    "trend_mean=station[pd.array(station['time1'])<str(2019)].groupby(['week']).mean()['dayPower/capacity']\n",
    "trend_std=station['dayPower/capacity'].std()\n",
    "trend = []\n",
    "std = []\n",
    "for ix, row in station.iterrows():\n",
    "    trend.append(trend_mean[row.week])\n",
    "    std.append(trend_std)\n",
    "station['trend']=trend\n",
    "station['std']=std\n",
    "station[\"detrended\"]=(station['dayPower/capacity']-station['trend'])/station['std']#去趋势\n",
    "# print(station)\n",
    "print('station_%d'%(station[\"id\"][1]))"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# 导入天气信息"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[[-0.88702266  1.41169865 -0.95801008  1.73671087]\n",
      " [-0.88702266  0.67535425 -0.44239888  0.60279281]\n",
      " [-0.88702266  1.41169865  0.07321233  0.60279281]\n",
      " [-0.88702266  0.79807832 -0.44239888 -1.2114761 ]\n",
      " [-1.15107193  1.65714678 -1.47362129  1.73671087]\n",
      " [-0.88702266  0.67535425 -0.95801008  0.14922558]\n",
      " [-1.15107193  1.47306068  0.07321233  1.73671087]\n",
      " [-1.32710478  2.14804304 -0.95801008 -0.07755803]\n",
      " [-1.15107193  0.55263019 -1.47362129  0.60279281]\n",
      " [-1.50313763 -0.24507624  0.58882353  0.14922558]\n",
      " [-1.23908835  0.61399222  0.58882353  0.14922558]\n",
      " [-0.88702266 -0.06099014 -1.47362129  0.60279281]\n",
      " [-1.50313763 -0.98142064  0.58882353  0.60279281]\n",
      " [-1.50313763 -0.79733454  0.07321233  0.14922558]\n",
      " [-1.4151212   0.18445799 -1.47362129 -1.2114761 ]]\n"
     ]
    }
   ],
   "source": [
    "weather=pd.read_csv('jinan_weather_by_day.csv')\n",
    "weather['time1']=pd.to_datetime(weather['time'], format='%Y-%m-%d %H:%M').dt.strftime(\"%Y-%m-%d\")\n",
    "weather_sum=pd.DataFrame(weather.iloc[:,2:6])\n",
    "removed_mean = weather_sum.mean()\n",
    "removed_std = weather_sum.std()\n",
    "weather_sum=(weather_sum-removed_mean)/removed_std\n",
    "\n",
    "print(weather_sum.values[0:15,])\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# 评价函数"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {},
   "outputs": [],
   "source": [
    "def compute_error(trues, predicted):#    回归评价函数：MAE、RAE、RMSE、RRSE、MAPE、R2\n",
    "    corr = np.corrcoef(predicted, trues)[0,1]#计算皮尔逊积矩相关系数 r\n",
    "    \n",
    "#     皮尔逊相关系数是一种度量两个变量间（线性）相关程度的方法。它是一个介于 1 和 -1 之间的值，\n",
    "#     其中，1 表示变量完全正相关， 0 表示无关，-1 表示完全负相关。\n",
    "   \n",
    "    mae = np.mean(np.abs(predicted - trues))#平均绝对值误差（MAE）\n",
    "#      \"\"\"\n",
    "#     numpy.mean(a, axis, dtype, out，keepdims )   求取均值\n",
    "#     经常操作的参数为axis，以m * n矩阵举例：\n",
    "#     axis 不设置值，对 m*n 个数求均值，返回一个实数\n",
    "#     axis = 0：压缩行，对各列求均值，返回 1* n 矩阵\n",
    "#     axis =1 ：压缩列，对各行求均值，返回 m *1 矩阵\n",
    "#     操作对象为：数组或矩阵\n",
    "#     数组：\n",
    "#     a = np.array([[1, 2], [3, 4]])\n",
    "#     >>>np.mean(a, axis=0) # axis=0，计算每一列的均值\n",
    "#     array([ 2.,  3.])\n",
    "#     >>> np.mean(a, axis=1) # 计算每一行的均值 \n",
    "#     array([ 1.5,  3.5])\n",
    "#     \"\"\"\n",
    "    rae = np.sum(np.abs(predicted - trues)) / np.sum(np.abs(trues - np.mean(trues)))\n",
    "#      \"\"\"\n",
    "#     sum(a, axis=None, dtype=None, out=None, keepdims=np._NoValue)\n",
    "#     a：用于进行加法运算的数组形式的元素\n",
    "#     axis的取值有三种情况：1.None(在默认/缺省的情况下），2.整数， 3.整数元组。\n",
    "    \n",
    "#     如果axis取None，即将数组/矩阵中的元素全部加起来，得到一个和。\n",
    "#     如果axis为整数，axis的取值不可大于数组/矩阵的维度，且axis的不同取值会产生不同的结果。\n",
    "#     如果axis为整数元组（x，y），则是求出axis=x和axis=y情况下得到的和。\n",
    "#     \"\"\"     \n",
    "    rmse = np.sqrt(np.mean((predicted - trues)**2))#均方根误差（RMSE）\n",
    "    rrse = np.sqrt(np.sum((predicted - trues)**2) / np.sum((trues - np.mean(trues))**2)) #根相对平方误差（RRSE）\n",
    "    mape = np.mean(np.abs((predicted - trues) / trues)) * 100#平均绝对百分误差（MAPE） \n",
    "    r2 = max(0, 1 - np.sum((predicted - trues)**2) / np.sum((trues - np.mean(trues))**2))#R-平方  ,max 返回给定参数的最大值\n",
    "    return corr, mae, rae, rmse, rrse, mape, r2\n",
    "\n",
    "\n",
    "def compute_error_filtered(trues, predicted, filt):#计算错误已过滤   filt过滤器\n",
    "    trues = trues[filt]\n",
    "    predicted = predicted[filt]\n",
    "    corr = np.corrcoef(predicted, trues)[0,1]\n",
    "    mae = np.mean(np.abs(predicted - trues))\n",
    "    mse = np.mean((predicted - trues)**2)\n",
    "    rae = np.sum(np.abs(predicted - trues)) / np.sum(np.abs(trues - np.mean(trues)))\n",
    "    rmse = np.sqrt(np.mean((predicted - trues)**2))\n",
    "    r2 = max(0, 1 - np.sum((trues-predicted)**2) / np.sum((trues - np.mean(trues))**2))\n",
    "    return corr, mae, rae, rmse, rrse, mape, r2"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# 特征建立"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[[-1.62145593 -2.1809972  -0.74609156 -0.39995258 -2.08567909 -0.51939403\n",
      "  -0.574549   -0.77231442         nan         nan]\n",
      " [-0.96052241 -1.62145593 -2.1809972  -0.74609156 -0.39995258 -2.08567909\n",
      "  -0.51939403 -0.574549   -0.77231442         nan]\n",
      " [-0.01762859 -0.96052241 -1.62145593 -2.1809972  -0.74609156 -0.39995258\n",
      "  -2.08567909 -0.51939403 -0.574549   -0.77231442]\n",
      " [-0.64471168 -0.01762859 -0.96052241 -1.62145593 -2.1809972  -0.74609156\n",
      "  -0.39995258 -2.08567909 -0.51939403 -0.574549  ]\n",
      " [-1.14616784 -0.64471168 -0.01762859 -0.96052241 -1.62145593 -2.1809972\n",
      "  -0.74609156 -0.39995258 -2.08567909 -0.51939403]]\n",
      "(1144, 44)\n"
     ]
    }
   ],
   "source": [
    "lags = pd.concat([pd.Series(station[\"detrended\"]).shift(x) for x in range(0,NUM_LAGS)],axis=1).values #  NUM_LAGS = 10\n",
    "print(lags[7:12,])\n",
    "preds = pd.Series(station[\"detrended\"]).shift(-1).values\n",
    "trends = station[\"trend\"].values\n",
    "stds = station[\"std\"].values\n",
    "lags=lags[NUM_LAGS:-1]\n",
    "preds = preds[NUM_LAGS:-1]\n",
    "trends = trends[NUM_LAGS:-1]\n",
    "stds = stds[NUM_LAGS:-1]\n",
    "weather_feats = weather_sum.values[NUM_LAGS:-1,:]\n",
    "# print(weather_sum['temperature'].values[:15,])\n",
    "#想加入11天的天气特征\n",
    "weather_tendas_temperature=pd.concat([pd.Series(weather_sum['temperature']).shift(x) for x in range(0,NUM_LAGS+1)],axis=1).values\n",
    "weather_tendas_temperature=weather_tendas_temperature[NUM_LAGS:-1]\n",
    "weather_tendas_humidity=pd.concat([pd.Series(weather_sum['humidity']).shift(x) for x in range(0,NUM_LAGS+1)],axis=1).values\n",
    "weather_tendas_humidity=weather_tendas_humidity[NUM_LAGS:-1]\n",
    "weather_tendas_windSpeed=pd.concat([pd.Series(weather_sum['windSpeed']).shift(x) for x in range(0,NUM_LAGS+1)],axis=1).values\n",
    "weather_tendas_windSpeed=weather_tendas_windSpeed[NUM_LAGS:-1]\n",
    "weather_tendas_weatherCode=pd.concat([pd.Series(weather_sum['weatherCode']).shift(x) for x in range(0,NUM_LAGS+1)],axis=1).values\n",
    "weather_tendas_weatherCode=weather_tendas_weatherCode[NUM_LAGS:-1]\n",
    "weather_tendas=np.concatenate([weather_tendas_temperature,weather_tendas_humidity,weather_tendas_windSpeed,weather_tendas_weatherCode],axis=1)\n",
    "# s=0\n",
    "# for i in station[\"detrended\"] :\n",
    "    \n",
    "#     if int(i*100000)==int(-0.01762859*100000):\n",
    "#         print(i,s)\n",
    "#     if int(i*100000)==int(-0.64471168*100000):\n",
    "#         print(i,s)\n",
    "#     s+=1\n",
    "# print(weather_sum.values[:15,])\n",
    "# print(weather_tendas_temperature.shape)\n",
    "# print(lags.shape)\n",
    "print(weather_tendas.shape)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# 划分训练集，验证集，测试集"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 50,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[-1.14616784e+00 -3.47252363e-01  1.34843276e-01 -7.46582859e-01\n",
      " -1.24356958e+00 -1.39426710e+00 -1.39092695e+00 -1.32628877e+00\n",
      "  1.93110416e-01 -4.52776504e-02  2.05484893e-01 -6.26012117e-01\n",
      " -8.92501664e-01 -6.06114339e-01 -1.21049675e+00 -1.67242775e+00\n",
      " -1.10027165e+00  4.11337380e-01 -6.38877897e-01 -9.48232761e-02\n",
      "  1.27235146e-01  7.96565003e-01  4.63291805e-01  5.85145591e-01\n",
      "  7.58713452e-01 -2.35002670e+00  2.13955455e-01 -1.89269238e+00\n",
      " -2.58428994e-01  1.03049953e-01  5.33682470e-01  8.61639696e-01\n",
      "  6.86297488e-01 -9.43964785e-01 -1.81543585e-01  2.46181760e-02\n",
      "  1.29894899e-02  2.50635300e-01 -1.13255627e+00  2.35995173e-01\n",
      " -1.90889832e+00 -1.22367180e+00  1.09247795e+00  7.33473195e-01\n",
      "  8.55326981e-01  5.01397844e-01  4.93310782e-01  3.38345284e-02\n",
      " -1.15934290e+00  1.06674639e+00  5.01889147e-01  1.34843276e-01\n",
      "  5.14263624e-01  6.86297488e-01  1.39760726e+00  1.09216868e+00\n",
      "  8.99491246e-01  6.43412732e-01 -4.18385283e-01  6.68652989e-01\n",
      " -8.45465572e-03  1.03736540e+00  4.61745438e-01  3.59128468e-01\n",
      " -4.75910167e-01 -8.38749917e-02  8.48773916e-01 -4.33025410e-01\n",
      "  1.11456009e+00 -9.14831215e-02 -1.27482565e+00 -1.80108555e+00\n",
      " -1.95460895e-02  7.97310794e-01  1.45824431e+00  4.71271063e-01\n",
      " -4.25993413e-01 -9.01718016e-01  6.43412732e-01  1.39568976e+00\n",
      "  1.14468687e+00  8.14955293e-01 -1.44573022e+00 -7.34772144e-01\n",
      "  1.19540420e+00 -8.99367537e-02  1.48575022e+00  1.45346560e+00\n",
      " -1.75709801e-01  1.06309696e+00 -2.71542193e-02 -1.98244357e+00\n",
      "  3.21768221e-01  1.55007912e+00  1.08035796e+00  1.17519715e+00\n",
      "  1.26894945e+00  1.07930290e+00  1.22113576e+00  3.86097123e-01\n",
      "  1.58867647e+00  1.41486826e+00  6.87400269e-02  1.17888899e+00\n",
      "  9.12047753e-01  1.77436432e+00  1.29956753e+00  1.34422664e+00\n",
      "  7.07250331e-01  3.13189855e-01  1.24321789e+00  1.91564048e-01\n",
      "  3.07665346e-01  7.33473195e-01  1.62727381e+00  9.38834379e-01\n",
      " -3.94428069e-01  1.33327835e+00  6.67597925e-01  5.13517833e-01\n",
      "  1.17090973e+00  1.51148178e+00  9.77431720e-01  1.02080778e+00\n",
      "  1.59059396e+00  1.31088695e+00  1.20826998e+00  3.86097123e-01\n",
      "  9.19655883e-01  7.84445013e-01 -1.37112460e-01  1.02449962e+00\n",
      "  1.63253146e+00  1.64570651e+00  1.29956753e+00  1.22843461e+00\n",
      "  1.29907623e+00  5.83371245e-01  6.64257770e-01  1.05357134e+00\n",
      "  1.18253842e+00  1.41647294e-01  1.36995820e+00  1.05462640e+00\n",
      " -6.51743678e-01 -1.66444849e+00  1.28515539e+00  1.16967264e+00\n",
      "  8.36399438e-01 -2.51130138e-01  2.18350674e-01 -6.13146337e-01\n",
      " -8.19574956e-02  9.50645094e-01  4.36323150e-01  6.82010073e-01\n",
      "  7.90998078e-01  9.38834379e-01  7.63492171e-01  7.92915575e-01\n",
      "  6.67597925e-01 -1.85378577e+00 -7.33225776e-01  8.29595420e-01\n",
      "  8.61639696e-01  1.00794200e+00  1.03736540e+00  6.41866364e-01\n",
      "  3.71994248e-01 -2.95789241e-01  4.69353567e-01 -7.75622764e-02\n",
      "  4.80445001e-01 -3.13541544e-01  7.19061046e-01  9.89551709e-01\n",
      "  8.23533658e-01 -1.22892945e+00 -3.22012105e-01  1.21379449e+00\n",
      "  1.16602321e+00  9.24913534e-01  8.22296564e-01 -2.18594558e-01\n",
      " -1.26752679e+00 -8.75240664e-01 -5.99177775e-02 -1.53579069e+00\n",
      "  9.63510875e-01  8.48028124e-01 -2.44326119e-01 -1.02307696e+00\n",
      "  1.28290211e-01 -1.14064333e+00  4.45539503e-01 -8.11966826e-01\n",
      " -1.77659109e+00 -1.59523307e+00 -1.82075535e+00 -1.47993234e+00\n",
      " -2.27172923e-01 -4.80796689e-01  9.50645094e-01  1.07961217e+00\n",
      "  5.79083829e-01  8.33801540e-02  5.27129404e-01 -8.56493384e-02\n",
      "  3.81210600e-01 -5.03188096e-01  4.74920492e-01  1.28781514e-01\n",
      "  9.83984785e-01 -3.67593727e-04 -1.17924068e+00 -1.76285934e-02\n",
      " -9.14831215e-02 -2.84160555e-01 -1.73675665e+00 -9.58748062e-01\n",
      " -2.19085861e-01 -5.99177775e-02 -6.09454494e-01 -1.57104787e+00\n",
      " -1.22336253e+00 -1.11919919e+00 -1.20319789e+00  3.34142698e-01\n",
      "  9.56478878e-01  1.26251656e+00  1.31088695e+00  4.62054711e-01\n",
      "  7.73183921e-02  3.79293104e-01 -6.46964959e-02 -4.84488532e-01\n",
      " -6.48051835e-01 -1.42946243e-01  1.10534373e+00  4.63291805e-01\n",
      "  1.06117947e+00  4.88532063e-01  4.54713440e-01  1.06309696e+00\n",
      "  6.41866364e-01  5.26383613e-01  9.01841725e-02 -8.55821818e-01\n",
      "  1.79753332e-01  2.23129392e-01  6.12794648e-01  7.31926827e-01\n",
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      "  1.02783978e+00  9.50954368e-01  1.37676222e+00  1.17697149e+00\n",
      "  9.51700159e-01  3.38921416e-01 -1.76737473e+00  1.10503446e+00\n",
      "  1.83869322e+00  1.40249378e+00 -1.38331882e+00 -3.47743666e-01\n",
      "  1.18806293e+00  1.77071489e+00  1.24655804e+00  9.63820148e-01\n",
      " -1.29932012e+00  4.82219348e-01  1.17041843e+00  8.92149975e-01\n",
      "  9.47304940e-01  6.80463705e-01 -1.35202033e+00  6.82010073e-01\n",
      "  7.90998078e-01 -3.67593727e-04 -1.71960345e+00  1.75358113e-01\n",
      "  1.60679990e+00  1.54278027e+00  2.05976197e-01  6.36608713e-01\n",
      "  1.11895530e+00  9.17881536e-01  1.10169431e+00  1.33661851e+00\n",
      "  1.32406200e+00  1.24810441e+00  8.33801540e-02  1.06749218e+00\n",
      "  6.34834366e-01  1.39760726e+00  1.10503446e+00  8.89470783e-02\n",
      "  4.37560245e-01 -1.94941316e+00 -8.10911762e-01  1.27812339e+00\n",
      "  1.39760726e+00 -1.58391365e+00  9.50954368e-01  1.11944661e+00\n",
      "  4.30756226e-01 -7.59448640e-01  8.53552634e-01  1.36760772e-01\n",
      "  9.50645094e-01  2.94799565e-01 -1.15668315e-01  1.33136086e+00\n",
      "  9.38834379e-01  7.76357951e-01  1.23035211e+00  1.13076602e+00\n",
      "  1.37552512e+00  6.17681171e-01  1.22843461e+00  1.26047889e+00\n",
      "  1.20092871e+00  1.05023118e+00 -2.58738267e-01  7.57967661e-01\n",
      "  8.36399438e-01  9.58253224e-01 -1.11969049e+00 -6.38877897e-01\n",
      " -9.18233224e-01 -1.81543585e-01  5.26383613e-01 -1.15668315e-01\n",
      "  4.47828127e-02  5.01397844e-01  8.27821073e-01 -1.46286398e-01\n",
      " -7.47637924e-01  1.00241749e+00  7.72070536e-01  9.06790102e-01\n",
      "  9.51700159e-01 -5.48817434e-01  6.38526209e-01  8.86316192e-01\n",
      "  2.17604883e-01 -9.77675604e-01  4.47828127e-02  8.10176574e-01\n",
      "  6.86297488e-01  1.25608367e+00  1.05357134e+00  9.89551709e-01\n",
      "  9.26459901e-01  7.13803396e-01  2.05484893e-01 -5.99177775e-02\n",
      "  2.26821235e-01 -2.71542193e-02 -5.41476163e-01  1.02938614e+00\n",
      "  9.45387444e-01  1.09322374e+00 -8.56493384e-02 -1.07262259e+00\n",
      "  1.31088695e+00 -3.74221018e-01  2.57439318e-01 -1.69209755e+00\n",
      " -2.12322137e+00  1.84532051e-01  8.70110257e-01  4.10282316e-01\n",
      "  1.04101483e+00 -1.37651480e+00 -9.84479623e-01 -1.09395893e+00\n",
      "  2.48860953e-01  1.06309696e+00  1.11146735e+00  1.34979356e+00\n",
      "  1.04225193e+00  5.72279811e-01  8.74505476e-01  8.53552634e-01\n",
      "  4.71271063e-01  5.26074340e-01  7.83699222e-01  1.26097019e+00\n",
      "  1.21556883e+00  1.17041843e+00  4.03250318e-01  9.34439159e-01\n",
      "  1.40100926e-01 -6.57268187e-01 -8.10420459e-01 -1.61490286e+00\n",
      " -1.08109315e+00 -7.27835579e-02 -1.67731427e+00 -2.12427643e+00\n",
      " -1.77659109e+00  5.27620708e-01  1.15123993e+00  1.31194201e+00\n",
      "  1.30385495e+00 -1.44573022e+00  5.77537462e-01  5.52115174e-01\n",
      "  3.87210508e-02 -3.54056381e-01  1.13182109e+00  9.82210438e-01\n",
      "  9.60170720e-01  7.31926827e-01  3.07665346e-01  3.34634001e-01\n",
      " -1.86801235e-01 -9.04280568e-02 -7.93267263e-01 -4.29333568e-01\n",
      "  9.12047753e-01  9.25222807e-01  1.41647294e-01 -9.67407722e-02\n",
      " -6.05059275e-01  1.45934710e-01 -1.04689103e+00  5.51805901e-01\n",
      "  6.03578296e-01  4.50426025e-01 -2.76861698e-01  3.82297476e-02\n",
      " -3.41862166e-02  8.05781355e-01  3.07356072e-01 -1.71226218e+00\n",
      "  6.82010073e-01  7.13803396e-01  7.07250331e-01  6.34834366e-01\n",
      "  5.87063087e-01  1.27235146e-01  8.89470783e-02 -3.34386582e-01\n",
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      " -1.55712703e+00 -3.42964947e-01 -1.59152178e-01 -7.73369485e-01\n",
      " -1.98244357e+00 -8.36152019e-01 -1.15173477e+00 -1.09395893e+00\n",
      "  1.07337368e-01 -5.19394031e-01 -6.96174802e-01 -1.42636970e-01\n",
      " -8.74749361e-01 -4.69848405e-01 -3.60609446e-01 -4.84488532e-01\n",
      " -1.40713288e+00 -1.75116880e+00 -1.05610738e+00 -2.09699850e+00\n",
      " -1.34472148e+00 -1.98169778e+00 -2.28569779e+00 -5.96588713e-01\n",
      " -2.20147111e+00 -5.15744603e-01 -5.14507508e-01 -2.38264357e-01\n",
      " -9.65301127e-01 -8.70461945e-01 -6.09454494e-01 -1.04348902e-01\n",
      " -2.45563213e-01 -4.24447045e-01 -6.75700892e-01 -6.56522396e-01\n",
      " -5.61683215e-01 -3.13541544e-01 -1.31373226e+00 -5.92939285e-01\n",
      " -1.17066231e+00 -1.17746633e+00 -1.03293837e-01 -3.55830728e-01\n",
      " -1.00829369e+00 -1.13361134e+00 -8.63120675e-01 -2.56078515e-02\n",
      " -3.79787942e-01 -1.29025398e-01 -4.84488532e-01 -7.50978079e-01\n",
      " -6.18980120e-01 -9.66046918e-01 -1.26072277e+00 -2.38264357e-01\n",
      " -5.66461933e-01 -1.12777755e+00 -6.60917616e-01 -1.59677943e+00\n",
      " -8.37389114e-01 -2.22565631e+00]\n"
     ]
    }
   ],
   "source": [
    "i_train = 365*2#2017-2019\n",
    "i_val = 365*3#\n",
    "i_test = -1 # 2020-现在\n",
    "#训练数据\n",
    "lags_train = lags[:i_train,:]\n",
    "weather_feats_train = weather_feats[:i_train,:]\n",
    "y_train = preds[:i_train]\n",
    "weather_tendas_train=weather_tendas[:i_train,:]\n",
    "\n",
    "#验证数据\n",
    "lags_val = lags[i_train:i_val,:]\n",
    "weather_feats_val = weather_feats[i_train:i_val,:]\n",
    "y_val = preds[i_train:i_val]\n",
    "weather_tendas_val=weather_tendas[i_train:i_val,:]\n",
    "\n",
    "#测试数据\n",
    "lags_test = lags[i_val:i_test,:]\n",
    "weather_feats_test = weather_feats[i_val:i_test,:]\n",
    "y_test = preds[i_val:i_test]\n",
    "trend_test = trends[i_val:i_test]\n",
    "std_test = stds[i_val:i_test]\n",
    "weather_tendas_test=weather_tendas[i_val:i_test]\n",
    "\n",
    "\n",
    "print(y_train)\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# FN+station"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "共多少次  500\n",
      "最好的情况     139    Best: 0.6749364674907841\n",
      "验证集最好的情况 334    Best: 0.761951300705949\n",
      "(48, 1) (48,) (48,)\n",
      "MAE:  1.202\tRMSE: 1.516\tR2:   0.043\n"
     ]
    }
   ],
   "source": [
    "def build_model(num_inputs, num_lags, num_preds):#  MLP  多层感知机\n",
    "    input_lags = Input(shape=(num_lags,))\n",
    "    \n",
    "    x = input_lags\n",
    "    \n",
    "    x = BatchNormalization()(x)\n",
    "#     print('输出的x:',x)\n",
    "#BN算法可以省去这两项或者只需要小的L2正则化约束。原因，BN算法后，参数进行了归一化，\n",
    "#     原本经过激活函数没有太大影响的神经元，分布变得明显，经过一个激活函数以后，\n",
    "#     神经元会自动削弱或者去除一些神经元，就不用再对其进行dropout\n",
    "#     另外就是L2正则化，由于每次训练都进行了归一化，就很少发生由于数据分布不同导致的参数变动过大，带来的参数不断增大。\n",
    "#     可以吧训练数据集打乱，防止训练发生偏移\n",
    "#     在卷积中，会出现每层卷积层中有（L）多个特征图。AxAxL特征矩阵。我们只需要以每个特征图为单元求取一对γ、β。\n",
    "#     然后在对特征图进行神经元的归一化。\n",
    "\n",
    "#定义基本的网络层\n",
    "    x = Dense(units=100, activation=\"tanh\", kernel_regularizer=keras.regularizers.l2(0.05))(x)\n",
    "#     units:该层有几个神经元\n",
    "#     activation:该层使用的激活函数\n",
    "#     use_bias:是否添加偏置项\n",
    "#     kernel_initializer:权重初始化方法\n",
    "#     bias_initializer:偏置值初始化方法\n",
    "#     kernel_regularizer:权重规范化函数\n",
    "#     bias_regularizer:偏置值规范化方法\n",
    "#     activity_regularizer:输出的规范化方法\n",
    "#     kernel_constraint:权重变化限制函数\n",
    "#     bias_constraint:偏置值变化限制函数\n",
    "   \n",
    "    x = Dropout(0.5)(x)#这防止过拟合，按照0.5的比例去掉一些神经元。\n",
    "    preds = Dense(units=num_preds)(x)\n",
    "    \n",
    "    model = Model(input_lags, preds)\n",
    "    model.compile(loss=\"mse\", optimizer=\"adam\")#loss被称为损失函数，optimizer为优化器（编译神经网络的两个参数）\n",
    "    \n",
    "    return model, input_lags, preds\n",
    "checkpoint = ModelCheckpoint(\"weights2.best.hdf5\", monitor='val_loss', verbose=0, save_best_only=True, mode='min')\n",
    "# 该回调函数将在每个epoch后保存模型到filepath,filename：字符串，保存模型的路径\n",
    "# monitor：需要监视的值，通常为：val_acc 或 val_loss 或 acc 或 loss\n",
    "# verbose：信息展示模式，0或1。为1表示输出epoch模型保存信息，默认为0表示不输出该信息\n",
    "# save_best_only：当设置为True时，将只保存在验证集上性能最好的模型\n",
    "# mode：‘auto’，‘min’，‘max’之一，在save_best_only=True时决定性能最佳模型的评判准则，例如，当监测值为val_acc时，\n",
    "#     模式应为max，当检测值为val_loss时，模式应为min。在auto模式下，评价准则由被监测值的名字自动推断。\n",
    "# save_weights_only：若设置为True，则只保存模型权重，否则将保存整个模型（包括模型结构，配置信息等）\n",
    "# period：CheckPoint之间的间隔的epoch数\n",
    "model, input_lags, preds = build_model(1, NUM_LAGS, 1)\n",
    "model.fit(\n",
    "    np.concatenate([lags_train], axis=1),#前两年数据\n",
    "    y_train,\n",
    "    batch_size=64,\n",
    "    epochs=500,\n",
    "    validation_data=(np.concatenate([lags_val], axis=1), y_val),\n",
    "    callbacks=[checkpoint],\n",
    "    verbose=0)   \n",
    "print (\"共多少次 \", len(model.history.history[\"loss\"]))\n",
    "print (\"最好的情况    \", np.argmin(model.history.history[\"loss\"]), \"   Best:\", np.min(model.history.history[\"loss\"]))\n",
    "print (\"验证集最好的情况\", np.argmin(model.history.history[\"val_loss\"]), \"   Best:\", np.min(model.history.history[\"val_loss\"]))\n",
    "\n",
    "model.load_weights(\"weights2.best.hdf5\")\n",
    "\n",
    "preds_lstm = model.predict(np.concatenate([lags_test[:,:]], axis=1))\n",
    "print(preds_lstm.shape,std_test.shape,trend_test.shape)\n",
    "preds_lstm = preds_lstm[:,0] * std_test + trend_test\n",
    "y_true = y_test * std_test + trend_test\n",
    "corr, mae, rae, rmse, rrse, mape, r2 = compute_error(y_true, preds_lstm)\n",
    "print (\"MAE:  %.3f\\tRMSE: %.3f\\tR2:   %.3f\" % (mae, rmse, r2))"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# FN+Station+W"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "共多少次  500\n",
      "最好的情况     177    Best: 0.6658221257876044\n",
      "验证集最好的情况 197    Best: 0.7410340286281011\n",
      "MAE:  1.182\tRMSE: 1.499\tR2:   0.064\n"
     ]
    }
   ],
   "source": [
    "# checkpoint best model\n",
    "checkpoint = ModelCheckpoint(\"weights2.best.hdf5\", monitor='val_loss', verbose=0, save_best_only=True, mode='min')\n",
    "\n",
    "model, input_lags, preds = build_model(1, NUM_LAGS+4, 1)#在 input_lags, preds数据哟普变化\n",
    "model.fit(\n",
    "    #lags_train,\n",
    "    np.concatenate([lags_train, weather_feats_train[:,:4]], axis=1),\n",
    "    y_train,\n",
    "    batch_size=64,\n",
    "    epochs=500,\n",
    "    validation_data=(np.concatenate([lags_val, weather_feats_val[:,:4]], axis=1), y_val),#这个地方有变化\n",
    "    callbacks=[checkpoint],\n",
    "    verbose=0)   \n",
    "\n",
    "print (\"共多少次 \", len(model.history.history[\"loss\"]))\n",
    "print (\"最好的情况    \", np.argmin(model.history.history[\"loss\"]), \"   Best:\", np.min(model.history.history[\"loss\"]))\n",
    "print (\"验证集最好的情况\", np.argmin(model.history.history[\"val_loss\"]), \"   Best:\", np.min(model.history.history[\"val_loss\"]))\n",
    "\n",
    "model.load_weights(\"weights2.best.hdf5\")\n",
    "\n",
    "preds_lstm = model.predict(np.concatenate([lags_test[:,:], weather_feats_test[:,:4]], axis=1))\n",
    "preds_lstm = preds_lstm[:,0] * std_test + trend_test\n",
    "corr, mae, rae, rmse, rrse, mape, r2 = compute_error(y_true, preds_lstm)\n",
    "print (\"MAE:  %.3f\\tRMSE: %.3f\\tR2:   %.3f\" % (mae, rmse, r2))"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# FN+station+11天气"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 28,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "共多少次  500\n",
      "最好的情况     494    Best: 0.6420349018214501\n",
      "验证集最好的情况 382    Best: 0.7334953105612977\n",
      "MAE:  1.197\tRMSE: 1.498\tR2:   0.065\n"
     ]
    }
   ],
   "source": [
    "# checkpoint best model\n",
    "checkpoint = ModelCheckpoint(\"weights2.best.hdf5\", monitor='val_loss', verbose=0, save_best_only=True, mode='min')\n",
    "\n",
    "model, input_lags, preds = build_model(1, NUM_LAGS+4*11, 1)#在 input_lags, preds数据哟普变化\n",
    "model.fit(\n",
    "    #lags_train,\n",
    "    np.concatenate([lags_train, weather_tendas_train], axis=1),\n",
    "    y_train,\n",
    "    batch_size=64,\n",
    "    epochs=500,\n",
    "    validation_data=(np.concatenate([lags_val,weather_tendas_val], axis=1), y_val),#这个地方有变化\n",
    "    callbacks=[checkpoint],\n",
    "    verbose=0)   \n",
    "\n",
    "print (\"共多少次 \", len(model.history.history[\"loss\"]))\n",
    "print (\"最好的情况    \", np.argmin(model.history.history[\"loss\"]), \"   Best:\", np.min(model.history.history[\"loss\"]))\n",
    "print (\"验证集最好的情况\", np.argmin(model.history.history[\"val_loss\"]), \"   Best:\", np.min(model.history.history[\"val_loss\"]))\n",
    "\n",
    "model.load_weights(\"weights2.best.hdf5\")\n",
    "\n",
    "preds_lstm = model.predict(np.concatenate([lags_test[:,:],weather_tendas_test], axis=1))\n",
    "preds_lstm = preds_lstm[:,0] * std_test + trend_test\n",
    "corr, mae, rae, rmse, rrse, mape, r2 = compute_error(y_true, preds_lstm)\n",
    "print (\"MAE:  %.3f\\tRMSE: %.3f\\tR2:   %.3f\" % (mae, rmse, r2))"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# CNN-LSTM Station"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 37,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "共多少次  1000\n",
      "最好的情况     980    Best: 0.6934820167005878\n",
      "验证集最好的情况 963    Best: 0.7578916517022538\n",
      "MAE:  1.295\tRMSE: 1.583\tR2:   0.000\n",
      "179.27614972504705\n"
     ]
    }
   ],
   "source": [
    "def build_model(num_inputs, num_lags, num_preds):\n",
    "    input_lags = Input(shape=(num_lags,1))\n",
    "    \n",
    "    x = input_lags\n",
    "    x = LSTM(20, \n",
    "             kernel_regularizer=keras.regularizers.l2(0.1), \n",
    "             recurrent_regularizer=keras.regularizers.l2(0.1), \n",
    "             return_sequences=False)(x)\n",
    "    x = BatchNormalization()(x)\n",
    "    preds = Dense(units=num_preds, kernel_regularizer=keras.regularizers.l2(0.01))(x)\n",
    "    \n",
    "    model = Model(input_lags, preds)\n",
    "    model.compile(loss=\"mse\", optimizer=\"rmsprop\")\n",
    "    \n",
    "    return model, input_lags, preds\n",
    "\n",
    "checkpoint = ModelCheckpoint(\"weights2.best.hdf5\", monitor='val_loss', verbose=0, save_best_only=True, mode='min')\n",
    "\n",
    "model, input_lags, preds = build_model(1, NUM_LAGS, 1)\n",
    "model.fit(\n",
    "    lags_train[:,:,np.newaxis],\n",
    "    y_train,\n",
    "    batch_size=64,\n",
    "    epochs=1000,\n",
    "    validation_data=(lags_val[:,:,np.newaxis], y_val),\n",
    "    callbacks=[checkpoint],\n",
    "    verbose=0)   \n",
    "\n",
    "print (\"共多少次 \", len(model.history.history[\"loss\"]))\n",
    "print (\"最好的情况    \", np.argmin(model.history.history[\"loss\"]), \"   Best:\", np.min(model.history.history[\"loss\"]))\n",
    "print (\"验证集最好的情况\", np.argmin(model.history.history[\"val_loss\"]), \"   Best:\", np.min(model.history.history[\"val_loss\"]))\n",
    "\n",
    "# load weights\n",
    "model.load_weights(\"weights2.best.hdf5\")\n",
    "\n",
    "# make predictions\n",
    "preds_lstm = model.predict(np.concatenate([lags_test[:,:,np.newaxis]], axis=1))\n",
    "preds_lstm = preds_lstm[:,0] * std_test + trend_test\n",
    "y_true = y_test * std_test + trend_test\n",
    "corr, mae, rae, rmse, rrse, mape, r2 = compute_error(y_true, preds_lstm)\n",
    "print (\"MAE:  %.3f\\tRMSE: %.3f\\tR2:   %.3f\" % (mae, rmse, r2))\n",
    "print (mape)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "scrolled": true
   },
   "source": [
    "# CNN-LSTM Station+W"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 36,
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "共多少次  1000\n",
      "最好的情况     551    Best: 0.666045624262666\n",
      "验证集最好的情况 943    Best: 0.7270945044413005\n",
      "MAE:  1.224\tRMSE: 1.550\tR2:   0.000\n",
      "177.37959473797812\n"
     ]
    }
   ],
   "source": [
    "def build_model_weather(num_inputs, num_lags, num_feat, num_preds):\n",
    "    input_lags = Input(shape=(num_lags,1))\n",
    "    input_weather = Input(shape=(num_feat,))\n",
    "    \n",
    "    x_lags = input_lags\n",
    "    x_lags = LSTM(20, \n",
    "             kernel_regularizer=keras.regularizers.l2(0.1), \n",
    "             recurrent_regularizer=keras.regularizers.l2(0.1), \n",
    "             return_sequences=False)(x_lags)\n",
    "    x_lags = BatchNormalization()(x_lags)\n",
    "    \n",
    "    feat = Concatenate(axis=1)([x_lags, input_weather])\n",
    "    \n",
    "    feat = BatchNormalization()(feat)\n",
    "    #preds = Dense(units=num_preds)(feat)\n",
    "    preds = Dense(units=num_preds, kernel_regularizer=keras.regularizers.l2(0.01))(feat)\n",
    "    preds = Activation(\"linear\")(preds)\n",
    "    \n",
    "    model = Model([input_lags, input_weather], preds)\n",
    "    \n",
    "    rmsp = keras.optimizers.RMSprop(lr=0.001, rho=0.9, epsilon=1e-08, decay=0.0)\n",
    "    #model.compile(loss=\"mse\", optimizer=rmsp)\n",
    "    model.compile(loss=\"mse\", optimizer=\"rmsprop\")\n",
    "    \n",
    "    return model, input_lags, preds\n",
    "\n",
    "checkpoint = ModelCheckpoint(\"weights2.best.hdf5\", monitor='val_loss', verbose=0, save_best_only=True, mode='min')\n",
    "\n",
    "model, input_lags, preds = build_model_weather(1, NUM_LAGS, 4, 1)\n",
    "model.fit(\n",
    "    #lags_train, \n",
    "    #np.concatenate([lags_train, weather_feats_train[:,sel]], axis=1),\n",
    "    [lags_train[:,:,np.newaxis], weather_feats_train[:,:4]],\n",
    "    y_train,\n",
    "    batch_size=64,\n",
    "    epochs=1000,\n",
    "    validation_data=([lags_val[:,:,np.newaxis], weather_feats_val[:,:4]], y_val),\n",
    "    callbacks=[checkpoint],\n",
    "    verbose=0)   \n",
    "\n",
    "print (\"共多少次 \", len(model.history.history[\"loss\"]))\n",
    "print (\"最好的情况    \", np.argmin(model.history.history[\"loss\"]), \"   Best:\", np.min(model.history.history[\"loss\"]))\n",
    "print (\"验证集最好的情况\", np.argmin(model.history.history[\"val_loss\"]), \"   Best:\", np.min(model.history.history[\"val_loss\"]))\n",
    "\n",
    "model.load_weights(\"weights2.best.hdf5\")\n",
    "\n",
    "preds_lstm = model.predict([lags_test[:,:,np.newaxis], weather_feats_test[:,:4]])\n",
    "preds_lstm = preds_lstm[:,0] * std_test + trend_test\n",
    "y_true = y_test * std_test + trend_test\n",
    "corr, mae, rae, rmse, rrse, mape, r2 = compute_error(y_true, preds_lstm)\n",
    "print (\"MAE:  %.3f\\tRMSE: %.3f\\tR2:   %.3f\" % (mae, rmse, r2))\n",
    "print (mape)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# CNN-LSTM Station+11W"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 47,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "共多少次  1000\n",
      "最好的情况     973    Best: 0.6397896539675046\n",
      "验证集最好的情况 556    Best: 0.7286471358717305\n",
      "MAE:  1.277\tRMSE: 1.574\tR2:   0.000\n",
      "170.8769521850125\n"
     ]
    }
   ],
   "source": [
    "checkpoint = ModelCheckpoint(\"weights2.best.hdf5\", monitor='val_loss', verbose=0, save_best_only=True, mode='min')\n",
    "\n",
    "model, input_lags, preds = build_model_weather(1, NUM_LAGS, 4*11, 1)\n",
    "model.fit(\n",
    "    #lags_train, \n",
    "    #np.concatenate([lags_train, weather_feats_train[:,sel]], axis=1),\n",
    "    [lags_train[:,:,np.newaxis], weather_tendas_train],\n",
    "    y_train,\n",
    "    batch_size=64,\n",
    "    epochs=1000,\n",
    "    validation_data=([lags_val[:,:,np.newaxis], weather_tendas_val],y_val),\n",
    "    callbacks=[checkpoint],\n",
    "    verbose=0)   \n",
    "\n",
    "print (\"共多少次 \", len(model.history.history[\"loss\"]))\n",
    "print (\"最好的情况    \", np.argmin(model.history.history[\"loss\"]), \"   Best:\", np.min(model.history.history[\"loss\"]))\n",
    "print (\"验证集最好的情况\", np.argmin(model.history.history[\"val_loss\"]), \"   Best:\", np.min(model.history.history[\"val_loss\"]))\n",
    "\n",
    "model.load_weights(\"weights2.best.hdf5\")\n",
    "\n",
    "preds_lstm = model.predict([lags_test[:,:,np.newaxis], weather_tendas_test])\n",
    "preds_lstm = preds_lstm[:,0] * std_test + trend_test\n",
    "y_true = y_test * std_test + trend_test\n",
    "corr, mae, rae, rmse, rrse, mape, r2 = compute_error(y_true, preds_lstm)\n",
    "print (\"MAE:  %.3f\\tRMSE: %.3f\\tR2:   %.3f\" % (mae, rmse, r2))\n",
    "print (mape)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 55,
   "metadata": {
    "scrolled": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "[<matplotlib.lines.Line2D at 0x2d84d9612c8>]"
      ]
     },
     "execution_count": 55,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.plot(x['time'].values,x['dayPower/capacity'].values)\n",
    "#一年内的发电量数据随时间的变化"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 67,
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "[<matplotlib.lines.Line2D at 0x2d84e908b48>]"
      ]
     },
     "execution_count": 67,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "x=station.loc[station.index<365]\n",
    "plt.plot(x['time'].values,x['detrended'].values)\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 64,
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "[<matplotlib.lines.Line2D at 0x2d851fe5488>]"
      ]
     },
     "execution_count": 64,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.plot(y['time'].values,y['dayPower/capacity'].values)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 66,
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "[<matplotlib.lines.Line2D at 0x2d84e87f688>]"
      ]
     },
     "execution_count": 66,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "y=station.loc[station.index<186]\n",
    "plt.plot(y['time'].values,y['detrended'].values)\n",
    "#半年内的发电量随时间的变化"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Num GPUs Available:  0\n"
     ]
    }
   ],
   "source": [
    "\n",
    "print(\"Num GPUs Available: \", len(tf.config.experimental.list_physical_devices('GPU')))\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 150,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "False\n"
     ]
    }
   ],
   "source": [
    "import torch\n",
    "from torch.autograd import Variable\n",
    "print(torch.cuda.is_available())"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 147,
   "metadata": {
    "scrolled": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "False"
      ]
     },
     "execution_count": 147,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "tf.test.is_gpu_available()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 148,
   "metadata": {
    "scrolled": false
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[name: \"/device:CPU:0\"\n",
      "device_type: \"CPU\"\n",
      "memory_limit: 268435456\n",
      "locality {\n",
      "}\n",
      "incarnation: 17722720577744499620\n",
      "]\n"
     ]
    }
   ],
   "source": [
    "from tensorflow.python.client import device_lib\n",
    "print(device_lib.list_local_devices())"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  }
 ],
 "metadata": {
  "kernelspec": {
   "display_name": "Python 3",
   "language": "python",
   "name": "python3"
  },
  "language_info": {
   "codemirror_mode": {
    "name": "ipython",
    "version": 3
   },
   "file_extension": ".py",
   "mimetype": "text/x-python",
   "name": "python",
   "nbconvert_exporter": "python",
   "pygments_lexer": "ipython3",
   "version": "3.7.7"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 4
}
